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  Learning with Local and Global Consistency

Zhou, D., Bousquet, O., Lal, T., Weston, J., & Schölkopf, B. (2004). Learning with Local and Global Consistency. In S. Thrun, L. Saul, & B. Schölkopf (Eds.), Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference (pp. 321-328). Cambridge, MA, USA: MIT Press.

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 Creators:
Zhou, D1, 2, Author              
Bousquet, O1, 2, Author              
Lal, TN1, 2, Author              
Weston, J1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

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 Dates: 2004-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2333
 Degree: -

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Title: Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2003-12-08 - 2003-12-13

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Title: Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference
Source Genre: Proceedings
 Creator(s):
Thrun, S, Editor
Saul, LK, Editor
Schölkopf, B1, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: 1621 Volume / Issue: - Sequence Number: - Start / End Page: 321 - 328 Identifier: ISBN: 0-262-20152-6